A Focal load balancer based algorithm for task assignment in cloud environment

Mohammed, Mostafa Abdulghafoor; Hasan, Raed Abdulkareem; Ahmed, Munef Abdullah; Țăpuș, Nicolae; Shanan, Marwan Ali; Khaleel, Mohammad Khamees; Ali, Ahmed H.

doi:10.1109/ecai.2018.8679043

Cited by 31 publications

(29 citation statements)

References 13 publications

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“…Static strategies only operate very well when the load variance in the nodes is lower [9]. This method's operating period is considerably less than that of the dynamic load balancing system, or the actual work time provided by the algorithm [79]. In this algorithm, the main problem is the order constraint.…”

Section: Lb Approachesmentioning

confidence: 99%

A Comprehensive Study of Load Balancing Approaches in the Cloud Computing Environment and a Novel Fault Tolerance Approach

et al. 2020

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The past few years have witnessed the emergence of a novel paradigm called cloud computing. CC aims to provide computation and resources over the internet via dynamic provisioning of services. There are several challenges and issues associated with implementation of CC. This research paper deliberates on one of CC main problems i.e. load balancing (LB). The goal of LB is equilibrating the computation on the cloud servers such that no host is under/ overloaded. Several LB algorithms have been implemented in literature to provide effective administration and satisfying customer requests for appropriate cloud nodes, to improve the overall efficiency of cloud services, and to provide the end user with more satisfaction. An efficient LB algorithm improves efficiency and asset's usage through effectively spreading the workload across the system's different nodes. This review research paper objective is to present critical study of existing techniques of LB, to discuss various LB parameters i.e. throughput, performance, migration time, response time, overhead, resource usage, scalability, fault tolerance, power savings, etc. The research paper also discusses the problems of LB in the CC environment and identifies the need for a novel LB algorithm that employs FT metrics. It has been found that traditional LB algorithms are not good enough and they do not consider FT efficiency metrics for their operation. Hence, the research paper identifies the need for FT efficiency metric in LB algorithms which is one of the main concerns in cloud environments. A novel algorithm that employs FT in LB is therefore proposed.

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Section: Lb Approachesmentioning

confidence: 99%

A Comprehensive Study of Load Balancing Approaches in the Cloud Computing Environment and a Novel Fault Tolerance Approach

et al. 2020

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“…A classifier mainly aims at performing an accurate prediction of class values considering each instance in a set of data. The NBC [12,13] is a supervised classification technique which depends on the Bayes' Theorem to predict the class from the attributes of a dataset. Figure 2.…”

Section: Naïve Bayesmentioning

confidence: 99%

Efficient method for breast cancer classification based on ensemble hoffeding tree and naïve Bayes

Alhayali

Ahmed

Mohialden

et al. 2020

IJEECS

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<p><span>The most dangerous type of cancer suffered by women above 35 years of age is breast cancer. Breast Cancer datasets are normally characterized by missing data, high dimensionality, non-normal distribution, class imbalance, noisy, and inconsistency. Classification is a machine learning (ML) process which has a significant role in the prediction of outcomes, and one of the outstanding supervised classification methods in data mining is Naives Bayess Classification (NBC). Naïve Bayes Classifications is good at predicting outcomes and often outperforms other classifications techniques. Ones of the reasons behind this strong performance of NBC is the assumptions of conditional Independences among the initial parameters and the predictors. However, this assumption is not always true and can cause loss of accuracy. Hoeffding trees assume the suitability of using a small sample to select the optimal splitting attribute. This study proposes a new method for improving accuracy of classification of breast cancer datasets. The method proposes the use of Hoeffding trees for normal classification and naïve Bayes for reducing data dimensionality.</span></p>

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“…Physical Broadcast Channel (PBCH) uses three OFDMA symbols on 72 subcarriers in the middle of a channel every fourth LTE frame. PBCH is broadcasted every 40 ms and follows the synchronization signals [9,25,26]. PBCH carries system information for all UEs that contains the bandwidth, system frame number (SFN), and hybrid ARQ indication channel (PHICH) length [27,28].…”

Section: Physical Signals and Channelsmentioning

confidence: 99%

Analysis of LTE physical channels overhead

Al-Hilfi¹

2020

TELKOMNIKA

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LTE Network is the common mobile technology these days around the world and all service providers seek to how improve the network capacity and deliver the best performance in terms of delivered data rates and coverage area. LTE network consists of many protocols that work together to establish network connectivity, these protocols add variable headers that contains many control information that the network needs to operate. At the same time these headers decrease the effective capacity of the network, so there is a need to optimize the overhead size that used in various channels. The study will illustrate the different overheads that effect on the network capacity and investigate the effect of different values on achieving the best network capacity.

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A Focal load balancer based algorithm for task assignment in cloud environment

Cited by 31 publications

References 13 publications

A Comprehensive Study of Load Balancing Approaches in the Cloud Computing Environment and a Novel Fault Tolerance Approach

A Comprehensive Study of Load Balancing Approaches in the Cloud Computing Environment and a Novel Fault Tolerance Approach

Efficient method for breast cancer classification based on ensemble hoffeding tree and naïve Bayes

Analysis of LTE physical channels overhead

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